Mission: Iconic Reefs and National Coral Reef Monitoring Program Benthic Analysis

Authors:
Nicole Krampitz¹, Julia Mateski¹, Dione Swanson^2,3
¹ CSS, Inc. under contract to NOAA National Ocean Service, National Centers for Coastal Ocean Science
² NOAA National Marine Fisheries Service, Southeast Fisheries Science Center
³ University of Miami, Cooperative Institute for Marine and Atmospheric Studies

Data Sources: National Coral Reef Monitoring Program Florida Benthic Data

1 Introduction

Mission: Iconic Reefs (M:IR) is a multi-institutional initiative designed to restore ecological function and biodiversity across key reef sites in the Florida Keys. Led by NOAA in collaboration with federal, state, institutional, and non-profit partners, M:IR targets nearly three million square feet of reef at seven key coral reefs in the Florida Keys National Marine Sanctuary (FKNMS). The overarching M:IR goal is to restore coral cover and reef function to a self-sustaining state.

Monitoring the response of coral reef benthic communities and coral populations to reef restoration is essential to quantify changes over time from restoration (Goergen et al. 2022). For this comparison of the status of Iconic Reefs in comparison to Florida Keys reefs, National Coral Reef Monitoring Program (NCRMP) surveys were conducted in 2022 and 2024 by the FKNMS M:IR field team. NCRMP surveys are conducted on shallow (<30 m), hard-bottom reef habitats and employ a stratified-random, one-stage design within 50 m × 50 m grid cells to ensure representative sampling across depth and rugosity strata (Ault et al. 2021). The NCRMP survey design ensures that survey sites are allocated by cross-shelf zone, rugosity type, and depth strata with sites distributed around the Florida Keys from nearshore to offshore to a maximum depth of 30 m. The NCRMP sample grid was spatially joined with the seven distinct MIR zones (Figure 1): Carysfort (North and South), Cheeca Rocks, Eastern Dry Rocks, Horseshoe Reef, Looe Key, Newfound Harbor, and Sombrero Reef. Survey sites were proportionally allocated within NCRMP strata to maintain consistent representation across habitats.

Figure 1: Map of the Florida Keys showing the location of Mission:Iconic Reef’s restoration sites

Corals and benthic communities were monitored using two different NCRMP surveys: the Benthic Community Assessment survey and the Coral Demographics survey (NOAA CRCP 2022a, 2022b, 2024a, 2024b). The Benthic Community Assessment survey includes: 1) benthic cover (%) estimates along a 15-m transect with a line point intercept method, 2) presence/absence of Endangered Species Act (ESA)-listed coral species (NOAA National Marine Fisheries Service, 2014), 3) abundance of key macroinvertebrates, and 4) reef rugosity measurements within a 15 m x 2 m belt-transect area (NOAA CRCP 2022a, 2024b). At the same site, coral demographics were surveyed within a 10 m x 1 m belt-transect area (NOAA CRCP 2022b, 2024b). NCRMP coral demographic survey data were combined with complementarily allocated survey data from Florida Fish and Wildlife Conservation Commission’s Disturbance Response Monitoring (DRM; coraldrm.org). In all coral demographics surveys, all live coral colonies ≧ 4 cm were counted, identified to species, measured to the nearest centimeter, and estimates were made of the proportion per colony of any present mortality (recent or old), disease (absent, present: slow, fast), and/or bleaching (none, total, partial, paling). Only live coral colonies were included in the survey; dead colonies with 100% mortality were not surveyed (e.g., colonies killed by coral disease). Juvenile corals (< 4 cm) were reported for species richness only and were not included in counts, size measurements, or estimates of condition (NOAA CRCP 2022b); in 2024, juveniles of select coral species were counted (NOAA CRCP 2024b). In 2024, NCRMP benthic surveys also included large area imaging (LAI) collected at most survey sites: analyses of these LAI data are in progress and not presented here.

To allow direct comparisons of benthic communities inside the restoration areas with the Florida Keys reefs, the NCRMP dataset was restricted to strata types overlapping M:IR sites and depths less than 12 meters. A majority of the reef habitats within the seven M:IR areas ranged from 0-12 meters depth. Statistical comparisons were conducted between M:IR and NCRMP estimates for each survey year and between years for M:IR and NCRMP. This report focuses on coral populations and benthic community metrics; a separate report can be found for fish population assessments.

2 Benthic Cover Sites

Table 1. Number of benthic cover sites sampled inside and outside M:IR areas in each stratum.
Study Area	Strata Name	Strat Description	2022 Sample Number	2024 Sample Number
Inside (M:IR)	CFK01	Inshore reef, all relief types and depths	8	10
Inside (M:IR)	CFK02	Mid-channel patch reef, all relief types and depths	12	12
Inside (M:IR)	CFK03	Offshore patch reef, all relief types and depths	7	8
Inside (M:IR)	CFK04	Forereef, low relief, shallow (0-6 m)	NA	3
Inside (M:IR)	CFK05	Forereef, high relief, shallow (0-6 m)	24	33
Inside (M:IR)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	4	9
Inside (M:IR)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	32	28
Outside (NCRMP)	CFK01	Inshore reef, all relief types and depths	10	9
Outside (NCRMP)	CFK02	Mid-channel patch reef, all relief types and depths	21	13
Outside (NCRMP)	CFK03	Offshore patch reef, all relief types and depths	9	8
Outside (NCRMP)	CFK04	Forereef, low relief, shallow (0-6 m)	NA	6
Outside (NCRMP)	CFK05	Forereef, high relief, shallow (0-6 m)	7	9
Outside (NCRMP)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	9	7
Outside (NCRMP)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	10	28

3 Coral Demographic Sites

Table 2. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).
Study Area	Strata Name	Strat Description	2022 Sample Number	2024 Sample Number
Inside (M:IR)	CFK01	Inshore reef, all relief types and depths	12	10
Inside (M:IR)	CFK02	Mid-channel patch reef, all relief types and depths	16	12
Inside (M:IR)	CFK03	Offshore patch reef, all relief types and depths	7	11
Inside (M:IR)	CFK04	Forereef, low relief, shallow (0-6 m)	NA	4
Inside (M:IR)	CFK05	Forereef, high relief, shallow (0-6 m)	25	38
Inside (M:IR)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	4	12
Inside (M:IR)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	34	34
Outside (NCRMP + DRM)	CFK01	Inshore reef, all relief types and depths	27	24
Outside (NCRMP + DRM)	CFK02	Mid-channel patch reef, all relief types and depths	79	109
Outside (NCRMP + DRM)	CFK03	Offshore patch reef, all relief types and depths	37	42
Outside (NCRMP + DRM)	CFK04	Forereef, low relief, shallow (0-6 m)	4	31
Outside (NCRMP + DRM)	CFK05	Forereef, high relief, shallow (0-6 m)	21	45
Outside (NCRMP + DRM)	CFK06	Forereef, low relief, mid-shallow (6-12 m)	19	33
Outside (NCRMP + DRM)	CFK07	Forereef, high relief, mid-shallow (6-12 m)	60	61

4 Most Common and Focal M:IR Species

M:IR selected 10 focal coral species for comparison to NCRMP: Acropora palmata, Acropora cervicornis, Colpophyllia natans, Dendrogyra cylindrus, Dichocoenia stokesii, Diploria labyrinthiformis, Meandrina meandrites, Montastraea cavernosa, Pseudodiploria clivosa, and Pseudodiploria strigosa. The following table summarizes the number of observations across all stratum and survey years of each focal species and identifies the most abundant species encountered.

Table 3. Most Observed Species and Focal M:IR Species
Species	Species Code	Number of Colonies Observed
Siderastrea siderea	SID SIDE	9486
Porites astreoides	POR ASTR	4789
Stephanocoenia intersepta	STE INTE	2879
Porites porites	POR PORI	1334
Agaricia agaricites	AGA AGAR	1121
*Montastraea cavernosa	MON CAVE	1082
Siderastrea radians	SID RADI	510
Orbicella faveolata	ORB FAVE	503
Orbicella annularis	ORB ANNU	367
*Dichocoenia stokesii	DIC STOK	243
Porites furcata	POR FURC	237
Porites divaricata	POR DIVA	218
Solenastrea bournoni	SOL BOUR	192
*Colpophyllia natans	COL NATA	179
Eusmilia fastigiata	EUS FAST	120
*Diploria labyrinthiformis	DIP LABY	115
*Pseudodiploria strigosa	PSE STRI	96
*Acropora cervicornis	ACR CERV	87
*Pseudodiploria clivosa	PSE CLIV	54
*Meandrina meandrites	MEA MEAN	16
*Acropora palmata	ACR PALM	14
* indicates M:IR focal species

5 Benthic Cover Composition

Temporal trends in benthic cover of functional groups such as coral and macroalgae cover provide insight …..

Figure 2a-e. Hard coral and macroalgae cover estimates inside and outside M:IR areas for 2022 and 2024. Significant difference in cover estimates in each of these areas was indicated by (*).

6 Species Specific Plots

Insert more text here. Sentence about surveys designed to produce population estimates of coral species. Metrics for status, trends and restoration effects(?) - density, partial mortality, mean colony size, colony size frequency, and colony size partial mortality. Only focal M:IR species are reported below

The following figures for these metrics are grouped by each focal species. Significant statistical differences are indicated for differences between survey years and between areas (MIR vs. NCRMP) for density, old mortality, and mean colony size.

Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).

7 Bleaching Estimates

Code

# Set path to data files
data_2022_path <- "data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"



MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

#Script works for the actual value but CI are not exact
# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)


names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp_ble_2022 <- read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>%
  left_join(., names_clean, by = "SPECIES_CD")

top_species <- tmp_ble_2022 %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp_ble_2022 %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 

##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))
  

plt_bleach <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group )) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
  geom_errorbar(aes(xmin = PV_LCI, xmax = PV_UCI),
                position = position_dodge(width = 0.8), width = 0.1) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) +
  ggtitle("2022 Estimates")


print(plt_bleach)

Figure 4. Estimated Observed Bleaching Frequency in 2022 using Ratio Estimaters.

Code

# Set path to data files
data_2022_path <- "data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"


MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

#same as section above
# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)

names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp <- read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>%
    left_join(names_clean, by = "SPECIES_CD")

top_species <- tmp %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 
  
##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))

plt_bleach <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  geom_errorbar(aes(xmin = PV_LCI, xmax = PV_UCI),
                position = position_dodge(width = 0.8), width = 0.1) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
   scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) +
  ggtitle("2024")


print(plt_bleach)

Figure 5. Estimated Observed Bleaching Frequency in 2024 using Ratio Estimaters

8 Disease Estimates

Code

# Set path to data files
data_2022_path <- "../Project/data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"


MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)

names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp_dis_2022 <- read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>%
    left_join(names_clean, by = "SPECIES_CD")

top_species <- tmp_dis_2022 %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp_dis_2022 %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 
  
##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0( "*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))

plt_disease <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  geom_errorbar(
    aes(xmin = pmax(0, PV_LCI), xmax = PV_UCI),
    width = 0.15
  ) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
   scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) +
  ggtitle("2022")


print(plt_disease)

Figure 6 . Estimated Observed Disease Frequency in 2022 using Ratio Estimaters.

Code

# Set path to data files
data_2024_path <- "../Project/data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv"


MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE")

# tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>%
#   filter(PV_TYPE == "Bleach_All") %>%
#   left_join(., names)

names_clean <- names %>%
  select(SPECIES_CD, SPECIES_NAME) %>%
  distinct(SPECIES_CD, .keep_all = TRUE)

tmp_dis_2024 <- read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>%
    left_join(names_clean, by = "SPECIES_CD")


top_species <- tmp_dis_2024 %>%
  group_by(SPECIES_CD) %>%
  summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>%
  arrange(desc(mean_pv)) %>%
  slice_head(n = 10) %>%  
  pull(SPECIES_CD)

tmp_filtered <- tmp_dis_2024 %>%
  filter(SPECIES_CD %in% union(top_species, MIR_species)) 

##Add a * for those MIR species
tmp_filtered <- tmp_filtered %>% mutate(
  SPECIES_NAME = case_when(
    SPECIES_CD %in% MIR_species ~ paste0( "*", SPECIES_NAME),
    TRUE ~ as.character(SPECIES_NAME)))

#Factor re-order for aesthetics  
tmp_filtered <- tmp_filtered %>%
  mutate(SPECIES_NAME = 
           fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>%
    mutate(analysis_group = recode(analysis_group,
                                 "MIR_GRP" = "MIR",
                                 "NCRMP_GRP" = "NCRMP + DRM"))

plt_dis <- tmp_filtered %>%
  filter(SPECIES_NAME != "NA") %>%
  ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.8) +
  geom_errorbar(
    aes(xmin = pmax(0, PV_LCI), xmax = PV_UCI),
    width = 0.15) +
  facet_wrap(~analysis_group) +
    labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") +
    scale_x_continuous(expand = expansion(mult = c(0, 0.1))) +
        theme_Publication(base_size = 10) +
    coord_cartesian(clip = "off") +
        scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) +
   scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) +
    theme(
          strip.text = element_text(size = 14, face = "bold"),
          axis.text.y = element_text(size = 11),
          axis.title = element_text(size = 13),
          legend.position = "bottom",
          legend.text = element_text(size = 10),
          panel.spacing = unit(1, "lines")
        ) +
  ggtitle("2024")


print(plt_dis)

Figure 7 . Estimated Observed Disease Frequency in 2024 using Ratio Estimaters.

9 Overall Density Comparision

10 Overall Old Mortality Comparision

11 Overall Max Diameter Comparision

12 Point of Contact

Shay Viehman : NCRMP Atlantic Benthic Team Lead
shay.viehman@noaa.gov

13 References

Ault, J. S., Smith, S. G., Luo, J., Grove, L. J., Johnson, M. W., and Blondeau, J. (2021). Refinement of the southern Florida Reef Tract benthic habitat map with habitat use patterns of reef fish species. (NCEI Accession 0224176). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0224176 NOAA Coral Reef Conservation Program. (2022b).

National Coral Reef Monitoring Program (NCRMP) benthic community assessment survey field protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, and U.S. Virgin Islands–2022. NOAA Coral Reef Conservation Program. https://doi.org/0.25923/0708-8333

NOAA Coral Reef Conservation Program. (2022c). National Coral Reef Monitoring Program (NCRMP) coral demographics survey field protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, U.S. Virgin Islands. 2022. NOAA Coral Reef Conservation Program. https://doi.org/10.25923/9a1r-m911

NOAA National Marine Fisheries Service. (2014). Final rule. Endangered and threatened wildlife and plants: Final listing determinations on proposal to list 66 reef-building coral species and to reclassify elkhorn and staghorn corals. Federal Register, 79(175), 53851–54123.

insert fish tech memo referenced earlier

also missing some of the field protocols?

--- title: "Mission: Iconic Reefs and National Coral Reef Monitoring Program Benthic Analysis" subtitle: "Summary Report for 2022 and 2024" format: html: theme: cosmo toc: true toc-depth: 2 number-sections: true code-fold: true code-tools: true fig-cap-location: bottom fig-align: center self-contained: true title-block-banner: true title-block-style: default include-before-body: logos.html pdf: documentclass: article fig-cap-location: bottom includes: in_header: custom_header.tex editor: visual execute: freeze: auto echo: true warning: false message: false --- ## Introduction [Mission: Iconic Reefs](https://www.fisheries.noaa.gov/southeast/habitat-conservation/restoring-seven-iconic-reefs-mission-recover-coral-reefs-florida-keys) (M:IR) is a multi-institutional initiative designed to restore ecological function and biodiversity across key reef sites in the Florida Keys. Led by NOAA in collaboration with federal, state, institutional, and non-profit partners, M:IR targets nearly three million square feet of reef at seven key coral reefs in the Florida Keys National Marine Sanctuary (FKNMS). The overarching M:IR goal is to restore coral cover and reef function to a self-sustaining state. Monitoring the response of coral reef benthic communities and coral populations to reef restoration is essential to quantify changes over time from restoration (Goergen et al. 2022). For this comparison of the status of Iconic Reefs in comparison to Florida Keys reefs, National Coral Reef Monitoring Program (NCRMP) surveys were conducted in 2022 and 2024 by the FKNMS M:IR field team. NCRMP surveys are conducted on shallow (\<30 m), hard-bottom reef habitats and employ a stratified-random, one-stage design within 50 m × 50 m grid cells to ensure representative sampling across depth and rugosity strata (Ault et al. 2021). The NCRMP survey design ensures that survey sites are allocated by cross-shelf zone, rugosity type, and depth strata with sites distributed around the Florida Keys from nearshore to offshore to a maximum depth of 30 m. The NCRMP sample grid was spatially joined with the seven distinct MIR zones (Figure 1): Carysfort (North and South), Cheeca Rocks, Eastern Dry Rocks, Horseshoe Reef, Looe Key, Newfound Harbor, and Sombrero Reef. Survey sites were proportionally allocated within NCRMP strata to maintain consistent representation across habitats. ![Figure 1: Map of the Florida Keys showing the location of Mission:Iconic Reef's restoration sites](../Project/images/MIR%20Map.png){fig-align="center"} ```{r libraries, include = FALSE} # LIBRARIES library(ggplot2) library(dplyr) library(tidyr) library(cowplot) library(grid) library(patchwork) library(ggthemes) library(extrafont) library(grid) library(ggthemes) library(extrafont) library(plotly) library(tidyverse) library(ncrmp.benthics.analysis) library(ggplot2) library(cowplot) library(gridExtra) library(egg) library(mgcv) # source("R Scripts/total_dis_code.R") source("../Project/R Scripts/MIR_make_size_bins.R") source("../Project/R Scripts/theme_publication.R") source("../Project/R Scripts/QuadPanel.R") # source("R Scripts/bleaching_estimates.R") # source("R Scripts/total_bleach_code.R") source("../Project/R Scripts/dis_ble_prev_ratio_est.R") ``` ```{r MIR species, echo = FALSE} MIR_species <- c( "ACR CERV", "ACR PALM", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS_ AST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE" ) ``` Corals and benthic communities were monitored using two different NCRMP surveys: the Benthic Community Assessment survey and the Coral Demographics survey (NOAA CRCP 2022a, 2022b, 2024a, 2024b). The Benthic Community Assessment survey includes: 1) benthic cover (%) estimates along a 15-m transect with a line point intercept method, 2) presence/absence of Endangered Species Act (ESA)-listed coral species (NOAA National Marine Fisheries Service, 2014), 3) abundance of key macroinvertebrates, and 4) reef rugosity measurements within a 15 m x 2 m belt-transect area (NOAA CRCP 2022a, 2024b). At the same site, coral demographics were surveyed within a 10 m x 1 m belt-transect area (NOAA CRCP 2022b, 2024b). NCRMP coral demographic survey data were combined with complementarily allocated survey data from Florida Fish and Wildlife Conservation Commission's Disturbance Response Monitoring (DRM; coraldrm.org). In all coral demographics surveys, all live coral colonies ≧ 4 cm were counted, identified to species, measured to the nearest centimeter, and estimates were made of the proportion per colony of any present mortality (recent or old), disease (absent, present: slow, fast), and/or bleaching (none, total, partial, paling). Only live coral colonies were included in the survey; dead colonies with 100% mortality were not surveyed (e.g., colonies killed by coral disease). Juvenile corals (\< 4 cm) were reported for species richness only and were not included in counts, size measurements, or estimates of condition (NOAA CRCP 2022b); in 2024, juveniles of select coral species were counted (NOAA CRCP 2024b). In 2024, NCRMP benthic surveys also included large area imaging (LAI) collected at most survey sites: analyses of these LAI data are in progress and not presented here. To allow direct comparisons of benthic communities inside the restoration areas with the Florida Keys reefs, the NCRMP dataset was restricted to strata types overlapping M:IR sites and depths less than 12 meters. A majority of the reef habitats within the seven M:IR areas ranged from 0-12 meters depth. Statistical comparisons were conducted between M:IR and NCRMP estimates for each survey year and between years for M:IR and NCRMP. This report focuses on coral populations and benthic community metrics; a separate report can be found for fish population assessments. ## Benthic Cover Sites ```{r prepare data for benthic cover table, include = FALSE} ##Let's just start with MIR/NCRMP only (benthic cover) NCRMP_MIR <- read_csv("../Project/data/NCRMP_MIR_cora_2022_24_psu_grps.csv") %>% group_by(YEAR, strat_cora, analysis_group) %>% summarise(count = n()) %>% filter(analysis_group != "NCRMP_NA") %>% rename(STRAT = strat_cora, PROT = analysis_group) %>% mutate(description = case_when( STRAT == "CFK01" ~ "Inshore reef, all relief types and depths", STRAT == "CFK02" ~ "Mid-channel patch reef, all relief types and depths", STRAT == "CFK03" ~ "Offshore patch reef, all relief types and depths", STRAT == "CFK04" ~ "Forereef, low relief, shallow (0-6 m)", STRAT == "CFK05" ~ "Forereef, high relief, shallow (0-6 m)", STRAT == "CFK06" ~ "Forereef, low relief, mid-shallow (6-12 m)", STRAT == "CFK07" ~ "Forereef, high relief, mid-shallow (6-12 m)")) table <- NCRMP_MIR %>% pivot_wider(id_cols = STRAT:description, names_from = YEAR, values_from = count) %>% mutate(`Study area` = case_when( PROT == "MIR_GRP"~ "Inside (M:IR)", PROT == "NCRMP_GRP" ~ "Outside (NCRMP)" )) %>% select(`Study area`, STRAT, description, `2022`, `2024`) %>% arrange(`Study area`, STRAT) #conditional statement for rendering in both HTML and PDF if (knitr::is_html_output()) { DT::datatable(table, class = "cell-border stripe", rownames = FALSE, colnames = c("Study Area", "Strata Name", "Strata Description", "2022 Sample Number", "2024 Sample Number"), caption = "Table 1. Number of benthic cover sites sampled inside and outside M:IR areas in each stratum.", options = list( columnDefs = list(list(className = 'dt-center', targets = "_all")), info = FALSE, paging = FALSE, searching = FALSE ) )} name <- c("Study Area", "Strata Name", "Strat Description", "2022 Sample Number", "2024 Sample Number") invisible(table) ``` ```{r create benthic cover table, echo = FALSE} sjPlot::tab_df( table, title = "<div style='text-align:center'>Table 1. Number of benthic cover sites sampled inside and outside M:IR areas in each stratum.</div>", escape = FALSE, show.footnote = TRUE, alternate.rows = TRUE, col.header = name ) ``` ## Coral Demographic Sites ```{r create coral demo table data, include = FALSE} ###Demographics (including DRM) DRM <- read_csv("../Project/data/DRM_cora_2022_24_psu_grps.csv") %>% group_by(YEAR, strat_cora, analysis_group) %>% summarise(count = n()) %>% filter(analysis_group != "NCRMP_NA") %>% rename(STRAT = strat_cora, PROT = analysis_group) %>% mutate(description = case_when( STRAT == "CFK01" ~ "Inshore reef, all relief types and depths", STRAT == "CFK02" ~ "Mid-channel patch reef, all relief types and depths", STRAT == "CFK03" ~ "Offshore patch reef, all relief types and depths", STRAT == "CFK04" ~ "Forereef, low relief, shallow (0-6 m)", STRAT == "CFK05" ~ "Forereef, high relief, shallow (0-6 m)", STRAT == "CFK06" ~ "Forereef, low relief, mid-shallow (6-12 m)", STRAT == "CFK07" ~ "Forereef, high relief, mid-shallow (6-12 m)")) %>% rbind(., NCRMP_MIR) %>% group_by(STRAT, PROT, YEAR, description) %>% summarise(count = sum(count)) %>% ungroup() table_2 <- DRM %>% pivot_wider(id_cols = STRAT:description, names_from = YEAR, values_from = count) %>% mutate(`Study area` = case_when( PROT == "MIR_GRP"~ "Inside (M:IR)", PROT == "NCRMP_GRP" ~ "Outside (NCRMP + DRM)" )) %>% select(`Study area`, STRAT, description, `2022`, `2024`) %>% arrange(`Study area`, STRAT) if (knitr::is_html_output()) { DT::datatable(table_2, class = "cell-border stripe", rownames = FALSE, colnames = c("Study Area", "Strata Name", "Strata Description", "2022 Sample Number", "2024 Sample Number"), caption = "Table 2. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).", options = list( columnDefs = list(list(className = 'dt-center', targets = "_all")), info = FALSE, paging = FALSE, searching = FALSE ) )} invisible(table2) ``` ```{r print coral demo table, echo = FALSE} sjPlot::tab_df( table_2, title = "<div style='text-align:center'> Table 2. The number of coral demographic sites sampled inside and outside M:IR areas in each stratum (includes DRM data).</div>", escape = FALSE, show.footnote = TRUE, alternate.rows = TRUE, col.header = name ) ``` ## Most Common and Focal M:IR Species M:IR selected 10 focal coral species for comparison to NCRMP: *Acropora palmata*, *Acropora cervicornis*, *Colpophyllia natans*, *Dendrogyra cylindrus*, *Dichocoenia stokesii*, *Diploria labyrinthiformis*, *Meandrina meandrites*, *Montastraea cavernosa*, *Pseudodiploria clivosa*, and *Pseudodiploria strigosa*. The following table summarizes the number of observations across all stratum and survey years of each focal species and identifies the most abundant species encountered. ```{r make data for top species, include = FALSE} ##Everything calculated for density is done so by region/year, so maybe just do total colonies observed? df <- read.csv( "../Project/data/FK2024_NCRMP_DRM_MIR_corsz_AR_NK.csv") %>% filter(N > 0, analysis_group != "NCRMP_NA") %>% group_by(SPECIES_CD) %>% summarise(observed = n()) %>% arrange(desc(observed)) ##Top 15 species top <- df %>% slice(1:15) ##MIR Species target_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS_ AST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE") MIR <- df %>% filter( SPECIES_CD %in% target_species ) ##Together table <- full_join(MIR, top) ##Add names from origial DRM data names <- read_csv("../Project/data/DRM_FLK_2014_2024_2stage_coral_demographics.csv") %>% select(SPECIES_CD, SPECIES_NAME) %>% unique() final <- left_join(table, names) %>% select(SPECIES_NAME, SPECIES_CD, observed) %>% arrange(desc(observed)) %>% mutate(SPECIES_NAME = ifelse( `SPECIES_CD` %in% target_species, paste0("*", "<i>", SPECIES_NAME, "</i>"), paste0("<i>", SPECIES_NAME, "</i>") )) ``` ```{r create top species table, echo = FALSE} sjPlot::tab_df( final, title = "<div style='text-align:center'>Table 3. Most Observed Species and Focal M:IR Species</div>", alternate.rows = TRUE, show.footnote = TRUE, footnote = "* indicates M:IR focal species", col.header = c("Species", "Species Code", "Number of Colonies Observed") ) ``` ## Benthic Cover Composition Temporal trends in benthic cover of functional groups such as coral and macroalgae cover provide insight ..... ```{r fig 1: benthic cover data, fig.width=12, fig.asp=0.6, echo = FALSE} #get cover categories cat_codes <- read_csv("../Project/data/bcov_cat_codes.csv") %>% dplyr::rename(cover_group = CODE) #load and clean cover data (MIR and NCRMP, no DRM) cvr <- read_csv("../Project/data/fk2022_24_NCRMP_MIR_bcov_catcd_dom_NK.csv")%>% dplyr::rename( cover_group = COVER_CAT_CD, REGION = analysis_group, avCvr = wavp, SE = se_wp, n_sites = n ) %>% left_join(cat_codes) ``` ```{r fig_benthic_cover_data (OG), echo = FALSE} #wanted_cover_cats <- c("HCORA" , "MACAL") #Formatting Data for Combined Macroalgal/Hard Coral plot cvr_analysis <- cvr %>% group_by(REGION, COV_CAT_A, YEAR) %>% # filter(COV_CAT_A %in% wanted_cover_cats) %>% summarise( avCvr = sum(avCvr), SE = sqrt(sum(SE^2)) ) #change names --> more readable new_dat <- cvr_analysis %>% mutate(COV_CAT_A = recode(COV_CAT_A, "HCORA" = "Hard Coral", "MACAL" = "Macroalgae", "RAMIC" = "Ramicrusta", "TURFA" = "Turf Algae", "SPONG" = "Sponge", "SCORA" = "Octocoral", "CCALG" = "Crustose Coralline Algae (CCA)", "OTHER" = "Other" )) ##Other includes Cyanobacteria, Bare, Palythoa, Other organisms, Millepora, Seagrass ## read in significance sig <- read_csv("../Project/data/significance.csv") dodge_width <- 0.06 # Get unique categories categories <- unique(new_dat$COV_CAT_A) # Define the plot for multi-function use in tabset make_plot <- function(df, category) { df_filtered <- df %>% filter(COV_CAT_A == category) plot <- df_filtered %>% ggplot(aes(x = as.integer(YEAR), y = avCvr *100, group = interaction(COV_CAT_A, REGION), color = REGION)) + geom_point(size = 2, position = position_dodge(width = 0.06)) + geom_line(size = 1, position = position_dodge(width = 0.06)) + geom_errorbar(aes(ymin = avCvr*100 - SE*100, ymax = avCvr*100 + SE*100), width = 0.1, position = position_dodge(width = 0.06)) + scale_color_manual( values = c("MIR_GRP" = "#0072B2", "NCRMP_GRP" = "#D55E00"), labels = c("MIR", "NCRMP"), name = "" ) + labs(x = "Year", y = "Cover (%)", title = category ) + # facet_wrap(~ COV_CAT_A) + ##This was when we filtered for just hard corals/macroalgae. If we output as a pdf, might be necessary to bring back scale_x_continuous(breaks = c(2022, 2024)) + #scale_y_continuous(, labels = scales::percent_format(accuracy = 2) ) + theme_Publication(base_size = 20) + theme( strip.text = element_text(face = "bold", size = 16), legend.position = "bottom", legend.direction = "horizontal", legend.key.size = unit(1.2, "cm"), legend.margin = margin(0, 0, 0, 0, "cm"), legend.text = element_text(size = 16), legend.background = element_blank(), legend.title = element_blank() ) + guides(color = guide_legend(override.aes = list(size = 4))) ##Adding the significance labels #This may take a fair amount of tweaking ## sig_cover <- sig %>% filter(metric == "cover") %>% filter(variable == category) # For between years find a midpoint to go on the plot midpoints <- df_filtered %>% group_by(REGION) %>% summarise( y_mid = 1.02 * mean(avCvr * 100, na.rm = TRUE)) ## For between groups find the y min to go on the plot ymin <- df_filtered %>% group_by(COV_CAT_A) %>% summarise(y_min = 85 * (min(avCvr)- max(SE))) %>%#this hopefully gets a nice plot minimum just below wherever it ends dplyr::rename(variable = COV_CAT_A) #for joining purposes # Add them to the significane table sig_cover <- sig_cover %>% left_join(midpoints, by = "REGION") %>% left_join(ymin, by = "variable") ##Plot it with annotations (plot + ### Significance BETWEEN GROUPS on X axis geom_text(data = sig_cover %>% filter (is.na(REGION)), aes(x = YEAR, y = y_min), label = "*", inherit.aes = FALSE, size = 15, color = "black") + ### Significance BETWEEN YEARS on graph geom_text(data = sig_cover %>% filter (is.na(YEAR)), aes(x = 2023, y = y_mid, color = REGION), label = "*", inherit.aes = FALSE, size = 15)) } ``` ```{r loop through cover plots and tab results, fig.cap="Figure 2a-e. Hard coral and macroalgae cover estimates inside and outside M:IR areas for 2022 and 2024. Significant difference in cover estimates in each of these areas was indicated by (*).", fig.width=9, results='asis', fig.asp=0.8, echo=FALSE} # Loop through categories and print plots cat("::: {.panel-tabset}\n\n") # open tabset container # Define manual tab order ordered_categories <- c("Hard Coral", "Macroalgae", "Octocoral", "Sponge", "Turf Algae", "Crustose Coralline Algae (CCA)", "Ramicrusta", "Other") walk(ordered_categories, function(cat) { cat("### ", cat, " {.tab-pane}\n\n", sep = "") cat_plot <- make_plot(new_dat, cat) print(cat_plot) cat("\n\n") }) cat(":::\n") # close tabset container ``` ## Species Specific Plots Insert more text here. Sentence about surveys designed to produce population estimates of coral species. Metrics for status, trends and restoration effects(?) - density, partial mortality, mean colony size, colony size frequency, and colony size partial mortality. Only focal M:IR species are reported below The following figures for these metrics are grouped by each focal species. Significant statistical differences are indicated for differences between survey years and between areas (MIR vs. NCRMP) for density, old mortality, and mean colony size. ```{r prep density data, fig.width=12, fig.asp=0.6, echo = FALSE} # Read in clean dens data den <- read_csv("../Project/data/fk2022_24_NCRMP_MIR_corabd_dom_NK.csv") %>% rename( avDen = wavdns, SE = se_wdns, CV_perc = wcv_dns, n_sites = n) #load full species name names <- read_csv("../Project/data/NCRMP_FKEYS2022_Benthic_Data02_CoralDemographics.csv") %>% select(SPECIES_CD, SPECIES_NAME) #Join w species names den <- den %>% left_join(names, by = "SPECIES_CD") ``` ```{r find top species, echo = FALSE} # Load data to find top species AR_ready_demo <- read_csv("../Project/data/FK2024_NCRMP_DRM_MIR_corsz_AR_NK.csv") df <- AR_ready_demo %>% filter(N == 1) %>% group_by(SPECIES_CD) %>% summarise(total = n(), .groups = "drop") %>% arrange(desc(total)) %>% #filter(SPECIES_CD %in% codes) %>% slice(1:10) topspecies <- df$SPECIES_CD # Combine top species with MIR_species target_species <- unique(c(topspecies, MIR_species)) ``` ```{r prep data, echo = FALSE} tmp <- quad_panel_MIR(target_species) # Prepare Density Data a <- tmp$AR_density %>% select(YEAR, SPECIES_CD, wavdns, se_wdns, analysis_group) %>% rename(avDen = wavdns, SE_avDen = se_wdns) # Prepare old mort data b <- tmp$AR_mortality %>% filter(MORT_TYPE == "Old") %>% select(YEAR, SPECIES_CD, avMort, SE, analysis_group) %>% rename(avMortOld = avMort, SE_avMortOld = SE) # old_mortality_ready_for_stat_testing <- b %>% # mutate(UCI = avMortOld + SE_avMortOld, # LCI = avMortOld - SE_avMortOld) # # view(old_mortality_ready_for_stat_testing) # # readr::write_csv(old_mortality_ready_for_stat_testing, "old_mortality_ready_for_stat_testing.csv") # # dens_ready_for_stat_testing <- a%>% # mutate(UCI = avDen + SE_avDen, # LCI = avDen - SE_avDen) # # # readr::write_csv(dens_ready_for_stat_testing, "dens_ready_for_stat_testing.csv") # # # view(dens_ready_for_stat_testing) # Prepare size data # c <- tmp$AR_col_size %>% # mutate(SE_maxdiam = sqrt(var_maxdiam)) %>% # rename(avMaxdiam = avg_maxdiam) %>% # select(REGION, YEAR, SPECIES_CD, avMaxdiam, SE_maxdiam, analysis_group) #RATIO ESTIMATION ratio_est_2022 <- read_csv("../Project/data/fk2022_NCRMP_MIR_sppLbar_dom.csv") ratio_est_2024 <- read_csv("../Project/data/fk2024_NCRMP_MIR_sppLbar_domv2.csv") c <- bind_rows(ratio_est_2022, ratio_est_2024) %>% rename(avMaxdiam = Lbar, SE_maxdiam = SE_L) # Prep make size bins tmp <- MIR_make_size_bins(years = c(2022, 2024), analyzed_species = MIR_species) #gather and clean domain Est domain_estimates <- tmp$length_freq_domain_est %>% dplyr::mutate(YEAR = as.factor(YEAR)) %>% dplyr::ungroup() #gather and clean length demos demos <- tmp$length_demos %>% dplyr::mutate(YEAR = as.factor(YEAR)) %>% dplyr::ungroup() #get domain mortality data domain_mort <- tmp$domain_mort_spp # write_csv(a, "../Project/denisty_data.csv") # write_csv(b, "../Project/old_mort.csv") # write_csv(c, "../Project/max_diam.csv") ``` ```{r create_species_plots function, fig.width=9, fig.asp=1.2, echo = FALSE} create_species_plots <- function(species) { den_filtered <- a %>% filter(SPECIES_CD == species) b_filtered <- b %>% filter(SPECIES_CD == species) c_filtered <- c %>% filter(SPECIES_CD == species) if (nrow(den_filtered) == 0 || nrow(b_filtered) == 0 || nrow(c_filtered) == 0) { return(NULL) } dodge_width <- 0.05 ### DENSITY PLOT ########## plt1 <- den_filtered %>% ggplot(aes(x = YEAR, y = avDen, group = analysis_group, color = analysis_group)) + geom_point(position = position_dodge(dodge_width)) + geom_line(position = position_dodge(dodge_width)) + geom_errorbar(aes(ymin = avDen - SE_avDen, ymax = avDen + SE_avDen), width = 0.1, position = position_dodge(dodge_width)) + labs(y = "Density (col/m2)", x = "Sample Year", title = "Coral Density") + theme_Publication(base_size = 10) + theme(strip.text = element_text(face = "italic"))+ theme(legend.position = "none") + scale_x_continuous(breaks = c(2022, 2024)) + scale_color_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "Outside"), name = "") + scale_linetype_manual(values = c("solid", "dashed"), guide = "none") ##Adding the significance labels #This may take a fair amount of tweaking ## sig_cover <- sig %>% filter(metric == "density") %>% filter(variable == species) # For between years find a midpoint to go on the plot midpoints <- den_filtered %>% group_by(analysis_group) %>% summarise( y_mid = 1.02 * mean(avDen, na.rm = TRUE)) %>% dplyr::rename(REGION = analysis_group) #for joining purposes ## For between groups find the y min to go on the plot ymin <- den_filtered %>% group_by(SPECIES_CD) %>% summarise(y_min = .85 * (min(avDen)- max(SE_avDen))) %>%#this hopefully gets a nice plot minimum just below wherever it ends dplyr::rename(variable = SPECIES_CD) #for joining purposes # Add them to the significance table sig_cover <- sig_cover %>% left_join(midpoints, by = "REGION") %>% left_join(ymin, by = "variable") ##Plot it with annotations plt1 <- plt1 + ### Significance BETWEEN GROUPS on X axis geom_text(data = sig_cover %>% filter (is.na(REGION)), aes(x = YEAR, y = y_min), label = "*", inherit.aes = FALSE, size = 10, color = "black") + ### Significance BETWEEN YEARS on graph geom_text(data = sig_cover %>% filter (is.na(YEAR)), aes(x = 2023, y = y_mid, color = REGION), label = "*", inherit.aes = FALSE, size = 10) ### MORTALITY PLOT ########## plt2 <- b_filtered %>% ggplot(aes(x = YEAR, y = avMortOld, group = analysis_group, color = analysis_group)) + geom_point(position = position_dodge(dodge_width)) + geom_line(position = position_dodge(dodge_width)) + geom_errorbar(aes(ymin = avMortOld - SE_avMortOld, ymax = avMortOld + SE_avMortOld), width = 0.1, position = position_dodge(dodge_width)) + labs(y = "Old Mortality (%)", x = "Sample Year", title = "Old Mortality") + theme_Publication(base_size = 10) + theme(strip.text = element_text(face = "italic"))+ theme(legend.position = "none") + scale_x_continuous(breaks = c(2022, 2024)) + scale_color_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "Outside"), name = "") + scale_linetype_manual(values = c("solid", "dashed"), guide = "none") ##Adding the significance labels #This may take a fair amount of tweaking ## sig_cover <- sig %>% filter(metric == "mortality") %>% filter(variable == species) # For between years find a midpoint to go on the plot midpoints <- b_filtered %>% group_by(analysis_group) %>% summarise( y_mid = 1.02 * mean(avMortOld, na.rm = TRUE)) %>% dplyr::rename(REGION = analysis_group) #for joining purposes ## For between groups find the y min to go on the plot ymin <- b_filtered %>% mutate(YEARS = mean(YEAR)) %>% group_by(SPECIES_CD, YEARS) %>% summarise(y_min = .85 * (min(avMortOld)- max(SE_avMortOld))) %>% #changed here because APAL only has 2022 dplyr::rename(variable = SPECIES_CD) #for joining purposes # Add them to the significance table sig_cover <- sig_cover %>% left_join(midpoints, by = "REGION") %>% left_join(ymin, by = "variable") ##Plot it with annotations plt2 <- plt2 + ### Significance BETWEEN GROUPS on X axis geom_text(data = sig_cover %>% filter (is.na(REGION)), aes(x = YEAR, y = y_min), label = "*", inherit.aes = FALSE, size = 10, color = "black") + ### Significance BETWEEN YEARS on graph geom_text(data = sig_cover %>% filter (is.na(YEAR)), aes(x = YEARS, y = y_mid, color = REGION), label = "*", inherit.aes = FALSE, size = 10) plt3 <- c_filtered %>% ggplot(aes(x = YEAR, y = avMaxdiam, group = analysis_group, color = analysis_group)) + geom_point(position = position_dodge(dodge_width)) + geom_line(position = position_dodge(dodge_width)) + geom_errorbar(aes(ymin = avMaxdiam - SE_maxdiam, ymax = avMaxdiam + SE_maxdiam), width = 0.1, position = position_dodge(dodge_width)) + labs(y = "Mean Max Diameter (cm)", x = "Sample Year", title = "Colony Size") + theme_Publication(base_size = 10) + theme(strip.text = element_text(face = "italic"))+ theme(legend.position = "none") + scale_x_continuous(breaks = c(2022, 2024)) + scale_color_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "Outside"), name = "") + scale_linetype_manual(values = c("solid", "dashed"), guide = "none") ##Adding the significance labels #This may take a fair amount of tweaking ## sig_cover <- sig %>% filter(metric == "mortality") %>% filter(variable == species) # For between years find a midpoint to go on the plot midpoints <- c_filtered %>% group_by(analysis_group) %>% summarise( y_mid = 1.02 * mean(avMaxdiam), na.rm = TRUE) %>% dplyr::rename(REGION = analysis_group) #for joining purposes ## For between groups find the y min to go on the plot ymin <- c_filtered %>% group_by(SPECIES_CD) %>% summarise(y_min = .85 * (min(avMaxdiam)- max(SE_maxdiam))) %>%#this hopefully gets a nice plot minimum just below wherever it ends dplyr::rename(variable = SPECIES_CD) #for joining purposes # Add them to the significance table sig_cover <- sig_cover %>% left_join(midpoints, by = "REGION") %>% left_join(ymin, by = "variable") ##Plot it with annotations plt3 <- plt3 + ### Significance BETWEEN GROUPS on X axis geom_text(data = sig_cover %>% filter (is.na(REGION)), aes(x = YEAR, y = y_min), label = "*", inherit.aes = FALSE, size = 10, color = "black") + ### Significance BETWEEN YEARS on graph geom_text(data = sig_cover %>% filter (is.na(YEAR)), aes(x = 2023, y = y_mid, color = REGION), label = "*", inherit.aes = FALSE, size = 10) domain_sub = subset(domain_estimates, SPECIES_CD == species) demos_sub = subset(demos, SPECIES_CD == species) mort_sub = domain_mort %>% dplyr::filter(SPECIES_CD == species) domain_sub <- domain_sub %>% # add 0s in for species not observed tidyr::expand(., REGION, analysis_group, SPECIES_CD, YEAR, bin_num) %>% #filter(YEAR %in% years) %>% #Filter back to years analyzed only # connect back to demo_data to fill in with NAs dplyr::full_join(., domain_sub) %>% # make a presence/absence column dplyr::mutate(length_freq_domain = ifelse(is.na(length_freq_domain), 0, length_freq_domain)) %>% # add in mortality data dplyr::full_join(., mort_sub) n_bins = max(demos_sub$n_bins) text_size = 14 angle = 45 hjust = dplyr::if_else(angle == 45, 1, 0.5) min = min(demos_sub$min) bin_width = min(demos_sub$bin_width) small = dplyr::if_else(n_bins > 4, 0, 1) num_vec = seq_len(max(domain_sub$bin_num)) lab_vec <- c("4- 10", " 11-15", " 16-20", " 21-25", " 26-30", " 31-35", " 36-45", " 46-65", " 66-85", " 86-105", "106+") species_name <- names %>% filter(SPECIES_CD == species) %>% distinct(SPECIES_NAME) %>% pull(SPECIES_NAME) plt4 <- ggplot(data = domain_sub, aes(x = as.integer(bin_num), y = length_freq_domain, fill = analysis_group)) + geom_bar(stat = "identity", position = "dodge2", width = 0.9, color = "black", size = 0.5) + geom_point(aes(y = oldmort_domain, color = analysis_group), size = 2) + geom_line(aes(y = oldmort_domain, color = analysis_group), size = 2, alpha = 0.5) + facet_wrap(~YEAR) + labs(y = "Frequency (Size Class & Mortality)", x = "Length (cm)") + scale_x_continuous(breaks = seq_along(lab_vec), labels = lab_vec) + scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) + scale_color_manual(values = c("#0072B2", "#D55E00"), guide = "none") + theme_Publication(base_size = 10) + theme( axis.text.x = element_text(size = text_size, angle = angle, hjust = hjust), legend.title = element_blank(), strip.text = element_text(face = "italic")) #what goes between the three plots and the size freq/mort plot middle_annotation <- ggplot() + geom_text(aes(x = 0.5, y = 0.5, label = "Size Frequency Distributions"), size = 5, fontface = "bold") + theme_void() + theme(plot.margin = margin(0, 0, 0, 0)) library(magick) library(png) library(jpeg) species_images <- list( "Acropora cervicornis" = list(path = "../Project/coral_image_files/staghorn.jpg", type = "jpg"), "Colpophyllia natans" = list(path = "../Project/coral_image_files/col_nata.jpg", type = "jpg"), "Dichocoenia stokesii" = list(path = "../Project/coral_image_files/dic_stok.jpg", type = "jpg"), "Diploria labyrinthiformis" = list(path = "../Project/coral_image_files/dip_laby.jpg", type = "jpg"), "Meandrina meandrites" = list(path = "../Project/coral_image_files/mea_mean.jpg", type = "jpg"), "Pseudodiploria clivosa" = list(path = "../Project/coral_image_files/pse_cliv.png", type = "png"), "Pseudodiploria strigosa" = list(path = "../Project/coral_image_files/pse_stri.png", type = "png"), "Montastraea cavernosa" = list(path = "../Project/coral_image_files/mon_cave.jpg", type = "jpg"), "Acropora palmata" = list(path = "../Project/coral_image_files/acr_palm.png", type = "png") ) get_species_image_plot <- function(species_name) { info <- species_images[[species_name]] if (!is.null(info)) { img <- switch(info$type, png = png::readPNG(info$path), jpg = jpeg::readJPEG(info$path)) cowplot::ggdraw() + cowplot::draw_image(img) } else { ggplot() + theme_void() } } image_plot <- get_species_image_plot(species_name) final_plot <- (image_plot/ (plt1 + plt2 + plt3) / middle_annotation / plt4 ) + plot_layout(heights = c(1, 1, 0.1, 1.4)) + plot_annotation( title = paste0("<b><i>", species_name, "</i></b>"), subtitle = "Colony density, mortality, size, and size class distribution (2022 vs. 2024)", theme = theme( plot.title = ggtext::element_markdown(hjust = 0.5, size = 18), plot.subtitle = element_text(hjust = 0.5, size = 14) )) return(final_plot) } plots_list <- map(MIR_species, create_species_plots) names(plots_list) <- MIR_species lst_clean <- compact(plots_list) ```  ```{r coral-tabyss, echo=FALSE, fig.cap="Figures 3a-3i. Coral density, old mortality, colony size, and size frequency distribution for focal M:IR species across sample years (2022 and 2024) and survey types (MIR and NCRMP + DRM). Significant difference in cover estimates in each of these areas was indicated by (*).", results='asis', fig.width=9, fig.asp=1.2} cat("::: {.panel-tabset}\n\n") # open tabset container walk(seq_along(lst_clean), function(i) { plot <- lst_clean[[i]] species_code <- names(lst_clean)[[i]] species_name <- names %>% filter(SPECIES_CD == species_code) %>% distinct(SPECIES_NAME) %>% pull(SPECIES_NAME) cat("### <em>", species_name, "</em> {.tab-pane}\n\n") print(plot) }) cat("\n:::\n") # close tabset container ``` ## Bleaching Estimates ```{r bleach 2022, fig.cap = "Figure 4. Estimated Observed Bleaching Frequency in 2022 using Ratio Estimaters.", fig.width=9, fig.asp=1.2} # Set path to data files data_2022_path <- "data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv" MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE") #Script works for the actual value but CI are not exact # tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>% # filter(PV_TYPE == "Bleach_All") %>% # left_join(., names) names_clean <- names %>% select(SPECIES_CD, SPECIES_NAME) %>% distinct(SPECIES_CD, .keep_all = TRUE) tmp_ble_2022 <- read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>% left_join(., names_clean, by = "SPECIES_CD") top_species <- tmp_ble_2022 %>% group_by(SPECIES_CD) %>% summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>% arrange(desc(mean_pv)) %>% slice_head(n = 10) %>% pull(SPECIES_CD) tmp_filtered <- tmp_ble_2022 %>% filter(SPECIES_CD %in% union(top_species, MIR_species)) ##Add a * for those MIR species tmp_filtered <- tmp_filtered %>% mutate( SPECIES_NAME = case_when( SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME), TRUE ~ as.character(SPECIES_NAME))) #Factor re-order for aesthetics tmp_filtered <- tmp_filtered %>% mutate(SPECIES_NAME = fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>% mutate(analysis_group = recode(analysis_group, "MIR_GRP" = "MIR", "NCRMP_GRP" = "NCRMP + DRM")) plt_bleach <- tmp_filtered %>% filter(SPECIES_NAME != "NA") %>% ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group )) + geom_col(position = position_dodge(width = 0.8), width = 0.8) + scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) + geom_errorbar(aes(xmin = PV_LCI, xmax = PV_UCI), position = position_dodge(width = 0.8), width = 0.1) + facet_wrap(~analysis_group) + labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") + scale_x_continuous(expand = expansion(mult = c(0, 0.1))) + theme_Publication(base_size = 10) + coord_cartesian(clip = "off") + scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) + theme( strip.text = element_text(size = 14, face = "bold"), axis.text.y = element_text(size = 11), axis.title = element_text(size = 13), legend.position = "bottom", legend.text = element_text(size = 10), panel.spacing = unit(1, "lines") ) + ggtitle("2022 Estimates") print(plt_bleach) ``` ```{r bleach 2024, fig.cap = "Figure 5. Estimated Observed Bleaching Frequency in 2024 using Ratio Estimaters", fig.width=9, fig.asp=1.2} # Set path to data files data_2022_path <- "data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv" MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE") #same as section above # tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>% # filter(PV_TYPE == "Bleach_All") %>% # left_join(., names) names_clean <- names %>% select(SPECIES_CD, SPECIES_NAME) %>% distinct(SPECIES_CD, .keep_all = TRUE) tmp <- read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Ball_dom.csv") %>% left_join(names_clean, by = "SPECIES_CD") top_species <- tmp %>% group_by(SPECIES_CD) %>% summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>% arrange(desc(mean_pv)) %>% slice_head(n = 10) %>% pull(SPECIES_CD) tmp_filtered <- tmp %>% filter(SPECIES_CD %in% union(top_species, MIR_species)) ##Add a * for those MIR species tmp_filtered <- tmp_filtered %>% mutate( SPECIES_NAME = case_when( SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME), TRUE ~ as.character(SPECIES_NAME))) #Factor re-order for aesthetics tmp_filtered <- tmp_filtered %>% mutate(SPECIES_NAME = fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>% mutate(analysis_group = recode(analysis_group, "MIR_GRP" = "MIR", "NCRMP_GRP" = "NCRMP + DRM")) plt_bleach <- tmp_filtered %>% filter(SPECIES_NAME != "NA") %>% ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) + geom_col(position = position_dodge(width = 0.8), width = 0.8) + geom_errorbar(aes(xmin = PV_LCI, xmax = PV_UCI), position = position_dodge(width = 0.8), width = 0.1) + facet_wrap(~analysis_group) + labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") + scale_x_continuous(expand = expansion(mult = c(0, 0.1))) + theme_Publication(base_size = 10) + coord_cartesian(clip = "off") + scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) + scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) + theme( strip.text = element_text(size = 14, face = "bold"), axis.text.y = element_text(size = 11), axis.title = element_text(size = 13), legend.position = "bottom", legend.text = element_text(size = 10), panel.spacing = unit(1, "lines") ) + ggtitle("2024") print(plt_bleach) ``` ## Disease Estimates ```{r disease 2024, fig.cap = "Figure 6 . Estimated Observed Disease Frequency in 2022 using Ratio Estimaters.", fig.width=9, fig.asp=1.2} # Set path to data files data_2022_path <- "../Project/data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv" MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE") # tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>% # filter(PV_TYPE == "Bleach_All") %>% # left_join(., names) names_clean <- names %>% select(SPECIES_CD, SPECIES_NAME) %>% distinct(SPECIES_CD, .keep_all = TRUE) tmp_dis_2022 <- read_csv("../Project/data/fk2022_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>% left_join(names_clean, by = "SPECIES_CD") top_species <- tmp_dis_2022 %>% group_by(SPECIES_CD) %>% summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>% arrange(desc(mean_pv)) %>% slice_head(n = 10) %>% pull(SPECIES_CD) tmp_filtered <- tmp_dis_2022 %>% filter(SPECIES_CD %in% union(top_species, MIR_species)) ##Add a * for those MIR species tmp_filtered <- tmp_filtered %>% mutate( SPECIES_NAME = case_when( SPECIES_CD %in% MIR_species ~ paste0( "*", SPECIES_NAME), TRUE ~ as.character(SPECIES_NAME))) #Factor re-order for aesthetics tmp_filtered <- tmp_filtered %>% mutate(SPECIES_NAME = fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>% mutate(analysis_group = recode(analysis_group, "MIR_GRP" = "MIR", "NCRMP_GRP" = "NCRMP + DRM")) plt_disease <- tmp_filtered %>% filter(SPECIES_NAME != "NA") %>% ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) + geom_col(position = position_dodge(width = 0.8), width = 0.8) + geom_errorbar( aes(xmin = pmax(0, PV_LCI), xmax = PV_UCI), width = 0.15 ) + facet_wrap(~analysis_group) + labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") + scale_x_continuous(expand = expansion(mult = c(0, 0.1))) + theme_Publication(base_size = 10) + coord_cartesian(clip = "off") + scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) + scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) + theme( strip.text = element_text(size = 14, face = "bold"), axis.text.y = element_text(size = 11), axis.title = element_text(size = 13), legend.position = "bottom", legend.text = element_text(size = 10), panel.spacing = unit(1, "lines") ) + ggtitle("2022") print(plt_disease) ``` ```{r, fig.cap = "Figure 7 . Estimated Observed Disease Frequency in 2024 using Ratio Estimaters.", fig.width=9, fig.asp=1.2} # Set path to data files data_2024_path <- "../Project/data/FK2022_NCRMP_DRM_MIR_corsz_ARallv2.csv" MIR_species <- c("ACR PALM", "ACR CERV", "COL NATA", "DEN CYLI", "DIC STOK", "DIP LABY", "EUS FAST", "MEA MEAN", "PSE CLIV", "PSE STRI", "MON CAVE") # tmp <- disease_bleach_prevalence_ratio_est(data_2022_path, 2022) %>% # filter(PV_TYPE == "Bleach_All") %>% # left_join(., names) names_clean <- names %>% select(SPECIES_CD, SPECIES_NAME) %>% distinct(SPECIES_CD, .keep_all = TRUE) tmp_dis_2024 <- read_csv("../Project/data/fk2024_NCRMP_MIR_sppPVbar_Dall_dom.csv") %>% left_join(names_clean, by = "SPECIES_CD") top_species <- tmp_dis_2024 %>% group_by(SPECIES_CD) %>% summarise(mean_pv = mean(PVbar, na.rm = TRUE)) %>% arrange(desc(mean_pv)) %>% slice_head(n = 10) %>% pull(SPECIES_CD) tmp_filtered <- tmp_dis_2024 %>% filter(SPECIES_CD %in% union(top_species, MIR_species)) ##Add a * for those MIR species tmp_filtered <- tmp_filtered %>% mutate( SPECIES_NAME = case_when( SPECIES_CD %in% MIR_species ~ paste0( "*", SPECIES_NAME), TRUE ~ as.character(SPECIES_NAME))) #Factor re-order for aesthetics tmp_filtered <- tmp_filtered %>% mutate(SPECIES_NAME = fct_reorder(SPECIES_NAME, if_else(is.na(PVbar), 0, PVbar), .desc = FALSE)) %>% mutate(analysis_group = recode(analysis_group, "MIR_GRP" = "MIR", "NCRMP_GRP" = "NCRMP + DRM")) plt_dis <- tmp_filtered %>% filter(SPECIES_NAME != "NA") %>% ggplot(aes(x = PVbar, y = SPECIES_NAME, fill = analysis_group)) + geom_col(position = position_dodge(width = 0.8), width = 0.8) + geom_errorbar( aes(xmin = pmax(0, PV_LCI), xmax = PV_UCI), width = 0.15) + facet_wrap(~analysis_group) + labs(x = "Proportion of Colonies Bleached", y = "Coral Species", fill = "") + scale_x_continuous(expand = expansion(mult = c(0, 0.1))) + theme_Publication(base_size = 10) + coord_cartesian(clip = "off") + scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')"))) + scale_fill_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM")) + theme( strip.text = element_text(size = 14, face = "bold"), axis.text.y = element_text(size = 11), axis.title = element_text(size = 13), legend.position = "bottom", legend.text = element_text(size = 10), panel.spacing = unit(1, "lines") ) + ggtitle("2024") print(plt_dis) ``` ## Overall Density Comparision ```{r overall Density, fig.cap = "Figure 8. Caption needed here!", fig.width=9, fig.asp=1.2, echo = FALSE} ###Adding species names and starring MIR species a <- left_join(a, names %>% distinct(), by = "SPECIES_CD") %>% dplyr::mutate(SPECIES_NAME = case_when( SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME), TRUE ~ as.character(SPECIES_NAME))) #Factor re-order for aesthetics a <- a %>% mutate(SPECIES_NAME = fct_reorder(SPECIES_NAME, if_else(is.na(avDen), 0, avDen), .desc = FALSE)) plt1 <- a %>% ggplot(aes(x = avDen, y = SPECIES_NAME, group = interaction(SPECIES_CD, analysis_group), color = analysis_group)) + geom_point( position = position_dodge(width = 0.3)) + geom_errorbar(aes(xmin = avDen - SE_avDen, xmax = avDen + SE_avDen), width = 0.1, position = position_dodge(width = 0.3)) + labs(y = "Species", x = "Density (col/m2)") + theme_Publication(base_size = 10) + facet_grid(~YEAR) + scale_x_continuous(expand = expansion(mult = c(0, 0))) + scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')")))+ theme(strip.text = element_text(face = "italic"))+ scale_color_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM"), name = "") + scale_linetype_manual(values = c("solid", "dashed"), guide = "none") + theme( strip.text = element_text(size = 14, face = "bold"), axis.text.y = element_text(size = 11), axis.title = element_text(size = 13), legend.position = "bottom", legend.text = element_text(size = 10), panel.spacing = unit(1, "lines") ) print(plt1) ``` ## Overall Old Mortality Comparision ```{r overall Old_Mort, fig.cap = "Figure 9. Caption needed here!", fig.width=9, fig.asp=1.2, echo = FALSE} ###Adding species names and starring MIR species b <- left_join(b, names %>% distinct(), by = "SPECIES_CD") %>% dplyr::mutate(SPECIES_NAME = case_when( SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME), TRUE ~ as.character(SPECIES_NAME))) #Factor re-order for aesthetics b <- b %>% mutate(SPECIES_NAME = fct_reorder(SPECIES_NAME, if_else(is.na(avMortOld), 0, avMortOld), .desc = FALSE)) plt1 <- b %>% ggplot(aes(x = avMortOld, y = SPECIES_NAME, group = interaction(SPECIES_CD, analysis_group), color = analysis_group)) + geom_point( position = position_dodge(width = 0.3)) + scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')")))+ geom_errorbar(aes(xmin = avMortOld - SE_avMortOld, xmax = avMortOld + SE_avMortOld), width = 0.1, position = position_dodge(width = 0.3)) + labs(y = "Species", x = "Old Mortality (%)") + theme_Publication(base_size = 10) + facet_grid(~YEAR) + scale_x_continuous(expand = expansion(mult = c(0, 0))) + theme(strip.text = element_text(face = "italic"))+ scale_color_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "NCRMP + DRM"), name = "") + scale_linetype_manual(values = c("solid", "dashed"), guide = "none") + theme( strip.text = element_text(size = 14, face = "bold"), axis.text.y = element_text(size = 11), axis.title = element_text(size = 13), legend.position = "bottom", legend.text = element_text(size = 10), panel.spacing = unit(1, "lines") ) print(plt1) ``` ## Overall Max Diameter Comparision ```{r overall Max_Dim, fig.cap = "Figure 10. Caption needed here too!", fig.width=9, fig.asp=1.2, echo = FALSE} spec_unq <- unique(b$SPECIES_CD) ###Adding species names and starring MIR species c <- left_join(c, names %>% distinct(), by = "SPECIES_CD") %>% dplyr::mutate(SPECIES_NAME = case_when( SPECIES_CD %in% MIR_species ~ paste0("*", SPECIES_NAME), TRUE ~ as.character(SPECIES_NAME))) #Factor re-order for aesthetics c <- c %>% mutate(SPECIES_NAME = fct_reorder(SPECIES_NAME, if_else(is.na(avMaxdiam), 0, avMaxdiam), .desc = FALSE)) c <- c %>% filter(SPECIES_CD %in% spec_unq) plt1 <- c %>% ggplot(aes(x = avMaxdiam, y = SPECIES_NAME, group = interaction(SPECIES_CD, analysis_group), color = analysis_group)) + geom_point( position = position_dodge(width = 0.3)) + scale_y_discrete(labels = function(x) parse(text = paste0("italic('", x, "')")))+ geom_errorbar(aes(xmin = avMaxdiam - SE_maxdiam, xmax = avMaxdiam + SE_maxdiam), width = 0.1, position = position_dodge(width = 0.3)) + labs(y = "Species", x = "Average Mean Colony Size") + theme_Publication(base_size = 10) + facet_grid(~YEAR) + scale_x_continuous(expand = expansion(mult = c(0, 0))) + theme(strip.text = element_text(face = "italic"))+ scale_color_manual(values = c("#0072B2", "#D55E00"), labels = c("MIR", "Outside"), name = "") + scale_linetype_manual(values = c("solid", "dashed"), guide = "none") + theme( strip.text = element_text(size = 14, face = "bold"), axis.text.y = element_text(size = 11), axis.title = element_text(size = 13), legend.position = "bottom", legend.text = element_text(size = 10), panel.spacing = unit(1, "lines") ) print(plt1) ``` ## Point of Contact **Shay Viehman** : *NCRMP Atlantic Benthic Team Lead*\ [shay.viehman\@noaa.gov](mailto:shay.viehman@noaa.gov) ## **References** Ault, J. S., Smith, S. G., Luo, J., Grove, L. J., Johnson, M. W., and Blondeau, J. (2021). Refinement of the southern Florida Reef Tract benthic habitat map with habitat use patterns of reef fish species. (NCEI Accession 0224176). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0224176 NOAA Coral Reef Conservation Program. (2022b). National Coral Reef Monitoring Program (NCRMP) benthic community assessment survey field protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, and U.S. Virgin Islands--2022. NOAA Coral Reef Conservation Program. https://doi.org/0.25923/0708-8333 NOAA Coral Reef Conservation Program. (2022c). National Coral Reef Monitoring Program (NCRMP) coral demographics survey field protocols for U.S. Atlantic: Florida, Flower Garden Banks, Puerto Rico, U.S. Virgin Islands. 2022. NOAA Coral Reef Conservation Program. https://doi.org/10.25923/9a1r-m911 NOAA National Marine Fisheries Service. (2014). Final rule. Endangered and threatened wildlife and plants: Final listing determinations on proposal to list 66 reef-building coral species and to reclassify elkhorn and staghorn corals. Federal Register, 79(175), 53851--54123. *insert fish tech memo referenced earlier* also missing some of the field protocols?